Effects of Daytime Running Lamps on Pedestrians Visual Reaction Time: Implications on Vehicles and Human Factors
Bibliographic record
Abstract
The beneficial effects of Daytime Running Lamps (DRL) to avoid traffic accidents, especially those involving pedestrians and cyclists, have been known for some decades thanks to several pioneer studies analyzing the results yielded after the introduction of this function in some countries of the world. In spite of this proven efficacy, the question about potential negative effects related to the visual interaction between DRL and other functions in automotive lighting remain extremely important. This work describes a macro experiment carried out with 148 pedestrians in different situations involving turn indicator activation. The target of the experiment was the identification of factors influencing the Visual Reaction Time (VRT) of these observers when the turn indicator was activated in presence of lit DRL. The knowledge of these factors has a critical importance for carmakers, regulatory bodies in road and vehicle safety and drivers and pedestrians themselves, since VRT is an effective and widely used parameter in road safety to provide information about the probability of accident avoidance. Besides some vehicle and headlamp related variables found by means of an Analysis of Variance (ANOVA), some other variables inherent to pedestrians characteristics such as visual defects and gender, alone or combined with DRL color (white as required by law in ECE countries or amber as allowed in USA and Canada) were found to be statistically significant using Classification and Regression Tree (CART) as exploratory analysis, and a Generalized Linear Model (GLM) for validating the results. The conclusions of this pioneer study, not previously reported in the literature, point out that there is still very much to investigate with regards to Daytime Running Lamps, their design (distance to other functions), characteristics (color of light emitted) as well as their interaction with other functions of critical importance in automotive lighting such as turn indicators, but also on the human perception of this complex interactions. Our understanding and considerations about these findings could have a deep impact on road safety and vehicle design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".